BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

“…However, they are sometimes neglected due to the difficulty of implementing them. There is a lack of packages with the ability to perform inference for these models, particularly in python. …”

Missing Value Imputation in Relational Data Using Variational Inference by Simon Fontaine (7046618)

“…Additional results, implementation details, a Python implementation, and the code reproducing the results are available online. …”

Parallel Sampling of Decomposable Graphs Using Markov Chains on Junction Trees by Mohamad Elmasri (19421498)

“…We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG. …”

Compiled Global Dataset on Digital Business Model Research by Dimas Fauzan Aryadefa (22123186)

“…</p><p dir="ltr">For the modeling component, annual publication growth is projected from 2025–2034 using a logistic growth model (S-curve) implemented in Python. Outputs include both CSV tables and PNG charts that depict historical trends and forward-looking projections. …”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…<br><br><b>Missing-Data Handling & Imputation:</b></p><p dir="ltr">The following sequential steps were applied to create a complete and consistent daily time series suitable for analysis (presented in the Imputed_AP_Data_Zurich_2010-25 sheet), particularly addressing the absence of routine PM₂.₅ measurements prior to January 2016. The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …”